OFFERING
Physical AI
We build systems that see, guide and act in the physical world — vision, edge inference and autonomy on real production lines and in the field.
Physical AI puts models onto the factory floor, the charge point and the drone: cameras that verify each assembly step, models that run on-device at the edge, and autonomy that continues to work without the cloud.
The constraints are real time, variable conditions, PLC interlocks and safety. We have shipped vision error-proofing on automotive lines, EV charge-point orchestration and edge-first autonomous fleets.
Inside Physical AI.
Machine vision
We build vision systems that verify parts, monitor assembly and catch errors before they become defects.
Edge & devices
We run inference on-device, at the edge, where latency and connectivity rule out the cloud.
Line integration
We integrate with the line itself — guiding operators and interlocking with PLCs to stop errors in real time.
Autonomy & telemetry
We build autonomy and telemetry that hold up in the field, on real industrial protocols.
Representative work Aerpace (autonomous fleets), iris (EV charge points), automotive vision error-proofing. See case studies →
How an engagement runs
The steps of the engagement, and how we charge for them.
On-site assessment
We study the line or the field environment: the vision or edge use case, and the constraints of PLC interlocks, safety, connectivity, lighting and throughput.
Pilot on one line
We stand up a working vision or edge system on a single line or use case and prove it under production conditions.
Deploy & integrate
We harden it, interlock with the PLC and operators, and put it into production where it stops errors in real time.
Scale across lines
We roll the proven system out across lines and sites, with the telemetry to keep it reliable.
Engagement opens with a paid on-site assessment, then a fixed-scope pilot. Deployment and roll-out are scoped per line or site, with optional ongoing support for systems running in production.